Disclaimer:

These are my personal views and are meant for Informational purpose only. Please verify the Information via Professional help or via Official references before acting upon the information provided in this Blog.

Now, Here’s Term Based Relation’s in action

Scenario:

After data profiling you realize that there are records such as “my Company Inc.” and “my Company Incorporated” – so you set out to automatically find these mismatches in terms inside a value and correct them.

BEFORE cleaned data, the report showed that “my company Inc” revenue is less than that of Google:

AFTER cleaned data, the report correctly shows that “my company Inc” revenue is more than that of Google:

Steps taken to clean data:

(Just an overview, not covered in a step by step fashion)

1. Created the Knowledge Base w/ Two domains Company Names & Revenue

2. Term Based Relations Tab of Company Names domain:

3. Published the KB > Let’s create a DQS project

4. Mapped the domains:

5. DQS cleaned following records:

6. Exported the data and created a report out of clean data-set!

Business user is happy 🙂

conclusion:

In this post, we saw how to correct a word/term within a value in a domain. The example we used was Inc. , Inc and Incorporated . It can be used to correct terms like Jr. and Junior. Sr. and Senior. etc. Things like this are difficult to catch during data entry – But using Term Based Relations, a person who knows the Data can clean it so that it generates correct reports.

After all reports like this are of little to no use, are they? So Let’s NOT create confusing reports anymore.

Please note:

The revenue figures shown are just for demo purposes. I pulled up these numbers from Wikipedia. Please don’t make any financial decision based on these reports and if you do, I am not responsible for that.

The name “my Company Inc” is a fictional firm. It’s not any firm that I am/was associated with in past, future of present. It’s a fictional name!

And Writing disclaimers like this are NO fun – sucks the joy out of “Thinking out Loud” 🙂

In this blog – post, I’ll share a quick demo of how you can use Regular Expressions in Data Quality services to clean records. For the purpose of the demo, I’ll show how to clean perform a preliminary cleaning procedure on email records.

Before we begin, just a primer on Regular Expressions:

“a regular expression provides a concise and flexible means to match (specify and recognize) strings of text, such as particular characters, words, or patterns of characters. Common abbreviations for “regular expression” include regex and regexp.” – source: WikiPedia Regular Expressions

Let’s take an example:

using Regular Expressions you can:

check if a text starts with A or a

check if a text contains the word “data”

check if a text follows the following pattern: something@something.something.

among other things..

Now, Note the power of Regular Expressions to check for a pattern like something@something.com – now can we not use it to perform a basic cleaning on email records? I think, we can! and so, let’s try that:

for the purpose of the blog post, let me quickly show how you can create a Domain in a Knowledge Base that uses Regular Expressions to clean email records:

Note: This is just a basic demo to show Regular Expressions in action. For cleaning Email records, there are other options like using third-party data sets or writing an advanced regular expression. The RegEx I shared was just meant for quick demo but if you want to read more about Regular Expressions used to clean emails then I will strongly recommend you to read email regular expressions that I got to know from Erik Eckhardt via the discussion on a Beyond Relational Tip that I had posted.

Conclusion:

In this blog post, we saw how to do basic cleaning on email records using regular expressions in Data Quality Services

And a composite domain: Full Address which contains domains: Address line, city, state and zip in the composite domains.

6) And for the composite domain Full Address – this is how you configure reference data-sets:

7) After creating the Knowledge Base, start a new DQS project. Here the cleaning happens at the composite domain level and this is a demo of how DQS uses third party reference data-set to classify records as suggested, new, invalid, corrected or correct:

You can see that DQS corrected few records and mentions that the reason in this case was Melissa data’s address verify reference data-set: